






import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
							import torch


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE__				     =			logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__				     =			{'''vocab_file''': '''spiece.model'''}

SCREAMING_SNAKE_CASE__				     =			{
    '''vocab_file''': {
        '''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
    }
}

SCREAMING_SNAKE_CASE__				     =			{
    '''AI-Sweden/gpt-sw3-126m''': 2048,
    '''AI-Sweden/gpt-sw3-350m''': 2048,
    '''AI-Sweden/gpt-sw3-1.6b''': 2048,
    '''AI-Sweden/gpt-sw3-6.7b''': 2048,
    '''AI-Sweden/gpt-sw3-20b''': 2048,
}






class 			_UpperCamelCase(       __lowerCamelCase		):
		__SCREAMING_SNAKE_CASE  :       Any							=							VOCAB_FILES_NAMES
		__SCREAMING_SNAKE_CASE  :       List[Any]							=							PRETRAINED_VOCAB_FILES_MAP
		__SCREAMING_SNAKE_CASE  :       Tuple							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__SCREAMING_SNAKE_CASE  :       Any							=							['''input_ids''', '''attention_mask''']

		def __init__(					self		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]=False    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]=False    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]=False    ,       SCREAMING_SNAKE_CASE__		:    str=None    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]=None    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]=None    ,       SCREAMING_SNAKE_CASE__		:    List[str]=None    ,       SCREAMING_SNAKE_CASE__		:    Optional[Dict[str, Any]] = None    ,       **SCREAMING_SNAKE_CASE__		:    List[Any]    ,       ):



					'''simple docstring'''



					__a						:  Union[str, Any]									=     {} if sp_model_kwargs is None else sp_model_kwargs

					__a						:  List[Any]									=     kwargs.get('name_or_path'    )
					if name_or_path is None:
								logger.warning(
								    'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
								    ' you are testing the model, this can safely be ignored'    )
								__a						:  str									=     'None'

					# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
					__a						:  List[Any]									=     '<|endoftext|>' if eos_token is None else eos_token
					__a						:  List[str]									=     '<unk>' if unk_token is None else unk_token
					if "gpt-sw3-7b" in name_or_path:
								__a						:  str									=     unk_token if pad_token is None else pad_token
								__a						:  List[Any]									=     eos_token if bos_token is None else bos_token
					else:
								__a						:  Optional[Any]									=     '<pad>' if pad_token is None else pad_token
								__a						:  Tuple									=     '<s>' if bos_token is None else bos_token

					super().__init__(
					    do_lower_case=SCREAMING_SNAKE_CASE__    ,       remove_space=SCREAMING_SNAKE_CASE__    ,       keep_accents=SCREAMING_SNAKE_CASE__    ,       bos_token=SCREAMING_SNAKE_CASE__    ,       eos_token=SCREAMING_SNAKE_CASE__    ,       unk_token=SCREAMING_SNAKE_CASE__    ,       pad_token=SCREAMING_SNAKE_CASE__    ,       sp_model_kwargs=self.sp_model_kwargs    ,       **SCREAMING_SNAKE_CASE__    ,       )

					__a						:  str									=     do_lower_case
					__a						:  Dict									=     remove_space
					__a						:  Any									=     keep_accents
					__a						:  Optional[Any]									=     vocab_file

					__a						:  List[Any]									=     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
					self.sp_model.Load(SCREAMING_SNAKE_CASE__    )

					# Used for whitespace normalization in input texts
					# fmt : off
					__a						:  int									=     {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
					# fmt : on

					# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
					__a						:  List[Any]									=     re.compile(
					    f'''[{"".join(map(SCREAMING_SNAKE_CASE__    ,       list(range(0    ,       9    )    ) + list(range(1_1    ,       3_2    )    ) + list(range(1_2_7    ,       1_6_0    )    ) + [1_6_0, 1_7_3, 8_2_0_3]    )    )}]'''    )

		def __getstate__(					self		:    Optional[int]    ):



					'''simple docstring'''



					__a						:  List[Any]									=     self.__dict__.copy()
					__a						:  Union[str, Any]									=     None
					return state

		def __setstate__(					self		:    Any    ,       SCREAMING_SNAKE_CASE__		:    int    ):



					'''simple docstring'''



					__a						:  Union[str, Any]									=     d

					# for backward compatibility
					if not hasattr(self    ,       'sp_model_kwargs'    ):
								__a						:  Dict									=     {}

					__a						:  Union[str, Any]									=     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
					self.sp_model.Load(self.vocab_file    )

		@property
		# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ):



					'''simple docstring'''



					return len(self.sp_model    )

		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					__a						:  Optional[Any]									=     self.non_printing_characters_re.sub(''    ,       SCREAMING_SNAKE_CASE__    )

					# Normalize whitespaces
					__a						:  int									=     ''.join([char if char not in self.whitespaces else ' ' for char in text]    )

					# NFC Unicode normalization
					__a						:  Union[str, Any]									=     unicodedata.normalize('NFC'    ,       SCREAMING_SNAKE_CASE__    )
					return text

		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    str    ,       **SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					__a						:  Tuple									=     self.preprocess_text(SCREAMING_SNAKE_CASE__    )
					return self.sp_model.encode(SCREAMING_SNAKE_CASE__    ,       out_type=SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    int    ):



					'''simple docstring'''



					return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__    )

		@staticmethod
		def    __lowerCAmelCase  (					SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					return out_string

		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ):



					'''simple docstring'''



					__a						:  Any									=     []
					__a						:  List[Any]									=     ''
					__a						:  Optional[int]									=     False
					for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
											# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
											if not prev_is_special:
														out_string += " "

											out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__    ) + token
											__a						:  int									=     True
											__a						:  Tuple									=     []
								else:
											current_sub_tokens.append(SCREAMING_SNAKE_CASE__    )
											__a						:  str									=     False
					out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__    )

					return out_string

		def    __lowerCAmelCase  (					self		:    Dict    ):



					'''simple docstring'''



					__a						:  Optional[Any]									=     {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__    ): i for i in range(self.vocab_size    )}
					vocab.update(self.added_tokens_encoder    )
					return vocab

		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    str    ,       SCREAMING_SNAKE_CASE__		:    Optional[str] = None    ):



					'''simple docstring'''



					if not os.path.isdir(SCREAMING_SNAKE_CASE__    ):
								logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
								return
					__a						:  Tuple									=     os.path.join(
					    SCREAMING_SNAKE_CASE__    ,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

					if os.path.abspath(self.vocab_file    ) != os.path.abspath(SCREAMING_SNAKE_CASE__    ) and os.path.isfile(self.vocab_file    ):
								copyfile(self.vocab_file    ,       SCREAMING_SNAKE_CASE__    )
					elif not os.path.isfile(self.vocab_file    ):
								with open(SCREAMING_SNAKE_CASE__    ,       'wb'    ) as fi:
											__a						:  Any									=     self.sp_model.serialized_model_proto()
											fi.write(SCREAMING_SNAKE_CASE__    )

					return (out_vocab_file,)

		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    Union[str, List[str]]    ,       SCREAMING_SNAKE_CASE__		:    Union[str, bool] = False    ):



					'''simple docstring'''



					if isinstance(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ):
								__a						:  Tuple									=     self.preprocess_text(SCREAMING_SNAKE_CASE__    )
								__a						:  Dict									=     self.sp_model.encode(SCREAMING_SNAKE_CASE__    )
					else:
								__a						:  Optional[Any]									=     [self.preprocess_text(SCREAMING_SNAKE_CASE__    ) for t in text]
								__a						:  List[Any]									=     self.sp_model.encode(SCREAMING_SNAKE_CASE__    )

					if return_tensors is True or return_tensors == "pt":
								__a						:  Tuple									=     torch.tensor(SCREAMING_SNAKE_CASE__    )

					return token_ids

		def    __lowerCAmelCase  (					self		:    str    ,       SCREAMING_SNAKE_CASE__		:    Union[int, List[int]]    ):



					'''simple docstring'''



					return self.sp_model.decode(SCREAMING_SNAKE_CASE__    )



		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    "Conversation"    ):



					'''simple docstring'''



					__a						:  int									=     [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
					__a						:  int									=     (
					    f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(SCREAMING_SNAKE_CASE__    ) + f'''{self.bos_token}Bot:'''
					)
					return self.encode(text=SCREAMING_SNAKE_CASE__    )







def 							A__       (    lowerCamelCase = 4_00_00_00    )	->						int:
				UpperCamelCase_:				Dict						=    []
				UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    0, 1
				while b <= n:
								if b % 2 == 0:
												even_fibs.append(lowerCamelCase    )
								UpperCamelCase_, UpperCamelCase_:				int						=    b, a + b
				return sum(lowerCamelCase    )


if __name__ == "__main__":
							print(F"""{solution() = }""")






